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- Type:
- Document
- Description/Abstract:
- American History text to be used in A&S Interdisciplinary Studies online courses.
- Creator/Author:
- Bennett, Lawrence
- Submitter:
- Lawrence Bennett
- Date Uploaded:
- 10/24/2022
- Date Modified:
- 01/21/2026
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)
- Type:
- Document
- Description/Abstract:
- Modules to educate Officers in Officer I, II, III and Officer IV classes.
- Creator/Author:
- Bennett, Lawrence
- Submitter:
- Lawrence Bennett
- Date Uploaded:
- 08/18/2020
- Date Modified:
- 01/20/2026
- Date Created:
- 2020-08
- License:
- Attribution-NonCommercial 4.0 International
- Type:
- Document
- Description/Abstract:
- Case summaries involving EMS cases.
- Creator/Author:
- Bennett, Lawrence
- Submitter:
- Lawrence Bennett
- Date Uploaded:
- 07/10/2020
- Date Modified:
- 01/16/2026
- Date Created:
- 2020-07
- License:
- Attribution-NonCommercial 4.0 International
- Type:
- Generic Work
- Description/Abstract:
- This is the slide deck for the DTS Hands On Training.
- Creator/Author:
- Scherz, Thomas
- Submitter:
- Thomas Scherz
- Date Uploaded:
- 01/13/2026
- Date Modified:
- 01/13/2026
- Date Created:
- January 12, 2026
- License:
- Open Data Commons Public Domain Dedication and License (PDDL)
- Type:
- Document
- Description/Abstract:
- CHAPTERS 1- 18: Supplement to Prof. Larry Bennett’s textbook, FIRE SERVICE LAW (SECOND EDITION), Jan. 2017: http://www.waveland.com/browse.php?t=708 SEE ALSO RECENT PUBLISHED COURT DECISIONS [NOV. 2018-PRESENT] FOR 18 CHAPTERS: From Prof. Bennett’s Fire & EMS Law monthly newsletters [send him e-mail if wish to receive] https://scholar.uc.edu/concern/documents/n870zs553?locale=en
- Creator/Author:
- Bennett, Lawrence
- Submitter:
- Lawrence Bennett
- Date Uploaded:
- 07/27/2021
- Date Modified:
- 01/11/2026
- Date Created:
- 2021-07
- License:
- Attribution 4.0 International
- Type:
- Document
- Description/Abstract:
- Monthly newsletter for Fire and EMS.
- Creator/Author:
- Bennett, Lawrence
- Submitter:
- Lawrence Bennett
- Date Uploaded:
- 11/02/2020
- Date Modified:
- 01/11/2026
- Date Created:
- 2020-11
- License:
- Attribution 4.0 International
- Type:
- Dataset
- Description/Abstract:
- This is a dataset generated as a part of a research project studying the changing support among European Union (EU) members for the war in Ukraine. The dataset contains a number of conditions (variables) used to conduct fuzzy-set qualitative comparative analysis (fsQCA) to test five critical conditions that have shaped the change in public opinion that include economic growth, democratic rule, distance from the front lines, level of energy dependence from Russia and trust in social media. These conditions (or variables) include: Num: Case number in the row MEMBR: EU member state two or three-letter abbreviation WEALTH: GDP per capita in Euro (measured in purchasing power parties) as reported by Eurostat GROWTH: GDP growth in volume based on seasonally adjusted data by Eurostat DEMOCR: the overall score for each EU member’s democracy index for 2022. Data have been drawn from the Economist Intelligence Unit (EIU) 2022 report DISTAN: an average distance (in thousand kilometers) from the geographic center point of the national capital of each EU member-state to the south-western and north-eastern tips of the frontline of the war in Ukraine. I have accepted that the western tip of the frontline is Kinburnsʹka Kosa National Park (Geographic Coordinates: 46°34’37”N 31°30’44”E) and the eastern tip of the frontline is at the village of Topoli in Kharkiv Oblast (Geographic Coordinates: 49°57’52″N, 37°54′31″E). TRADE: volume of trade with Russia per capita in thousand of US Dollars. ENERGO: EU energy dependence on Russia as estimated by the European Commission (from 0 to 100 percent) for 2020. Source: Eurostat. GOVTR: Net trust in national government (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses). MEDIATR: Net trust in social media (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses). CNG_MIL -- change of net support for the financing of the purchase and supply of military equipment to Ukraine, Spring 2022-Spring 2025 CNG_FIN -- change of net support for the financing of support for Ukraine, Spring 2022-Spring 2025 CNG_HUM -- change of net support for providing humanitarian support for the people affected by the war, Spring 2022-Spring 2025 CNG_REF -- change of net support for welcoming in the EU people fleeing the war, Spring 2022-Spring 2025 AVCHNG: Difference in average change of the military, economic, humanitarian and refugee support for Ukraine Spring 2022-Spring 2025. WEALTH1: Calibrated score for national wealth (see paper for details) GROWT1: Calibrated score for economic growth (see paper for details) DEMOCR1: Calibrated score for democracy (see paper for details) DISTAN1: Calibrated score for distance (see paper for details) TRADE1: Calibrated score for trade (see paper for details) RENERGO1: Calibrated score for energy dependence (see paper for details) GOVTR1: Calibrated score for trust in governance (see paper for details) RMEDIATR1: Calibrated score for trust in social media (see paper for details) --------------- NB: File: "Dataset_UkrTime2yr.csv" contains data for the public support during the first two years (24 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2024 File"Dataset_UkrTime3yr.csv" contains data for the public support during the first three years (36 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2025
- Creator/Author:
- Ivanov, Ivan
- Submitter:
- Ivan Ivanov
- Date Uploaded:
- 04/27/2025
- Date Modified:
- 12/30/2025
- Date Created:
- 5-01-2024
- License:
- All rights reserved
- Type:
- Article
- Description/Abstract:
- Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research.
- Creator/Author:
- Debsarkar, Shyam
- Submitter:
- Shyam Debsarkar
- Date Uploaded:
- 12/28/2025
- Date Modified:
- 12/28/2025
- Date Created:
- March 25, 2025
- License:
- All rights reserved
- Type:
- Collection
- Description/Abstract:
- This collection includes a variety of resources compiled as a result of a study (#2324585) funded by the National Science Foundation. The study convened research-oriented and research enterprise professionals from social science fields, in a variety of ways, to discuss barriers, challenges, and solutions to increasing research funding at their institutions and beyond. Please see the "Helpful Online Resources" document for additional free resources. The organic repository will be reviewed and updated, if needed, annually. To suggest additional resources, please email ImpactAccelerator@uc.edu. This material is based upon work supported by the National Science Foundation under Grant Number 2324585. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
- Creator/Author:
- Green-Schwartz, Clair
- Submitter:
- Clair Green-Schwartz
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- Additional free resources for personnel working in the research enterprise.
- Creator/Author:
- Green-Schwartz, Clair
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- This document is a template for creating professional grant proposal timelines. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details.
- Creator/Author:
- Green-Schwartz, Clair and Foster, Daniel J.
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- This document is a template for creating professional grant proposal timelines. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details.
- Creator/Author:
- Green-Schwartz, Clair
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- This document is a template for creating the National Science Foundation's Graduate Student and Postdoctoral Mentoring Plan. Not all NSF grant proposals require this document. Be sure to check grant-specific requirements or instructions for more details.
- Creator/Author:
- Green-Schwartz, Clair and DenBleyker, Emma
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- This document is a template for creating one or two page project summaries. The template can be adopted for a variety of disciplines. Be sure to check grant- or funder-specific requirements or instructions for more details.
- Creator/Author:
- Green-Schwartz, Clair and DenBleyker, Emma
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Document
- Description/Abstract:
- This document is a template for creating letters of support. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details
- Creator/Author:
- Green-Schwartz, Clair
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/22/2025
- Date Modified:
- 12/22/2025
- License:
- Public Domain Mark 1.0
- Type:
- Media
- Description/Abstract:
- This presentation was created to share the preliminary results of this study, NSF #2324585, at the 2024 National Council of University Research Administrators Annual Meeting.
- Creator/Author:
- Green-Schwartz, Clair and Kennedy, Erica
- Submitter:
- Clair Green-Schwartz
- Date Uploaded:
- 12/15/2025
- Date Modified:
- 12/15/2025
- Date Created:
- 2024-08-04
- License:
- Public Domain Mark 1.0
- Type:
- Dataset
- Description/Abstract:
- Q2000 Deep Learning Model Package This Technical Resource Bundle provides the Q200 Deep Learning model for open access download and use. The Q2000 DL model is built to detect Maya structures in Lidar data visualized at one meter per pixel. Currently, this repository contains the ESRI ArcGIS compatible DL model in the format .dlpk. We expect to convert the model in its current form into Pytorch (.pt) and TensorFlow (.h5) formats to incude them also here for user access. The Q2000.dlpk file was created in 2025 at University of Cincinnati by Benjamin Britton, using ArcGIS Pro v.3.3 with data from the NASA Ames G-LiHT. It is intended as an experiment to evaluate the practicability of creating a broadscale deep learning model that can be used effectively to identify Maya structures in Lidar data across the length and breadth of the Yucatan Peninsula. The Q2000 model is the subject of an article, a draft of which is also included in this dataset, called "Evaluating Broadscale Deep Learning for Maya Settlement Detection in G-LiHT Lidar" which examines the process and rationale of the model development in detail. The article has been accepted for publication and this site is included in that article with a link to this permanent (DOI) publication site. To use the model with ArcGIS Pro, use a Lidar dataset converted to a 1m/pixel DEM file and visualized as a 3-channel RGB Hillshade or other customized visualization as source input. -Using the ArcGIS Pro Spatial Analyst Extension, the geoprocessing tool called "Detect Object Using Deep Learning" may be invoked. -For Input Raster, add your Lidar visualization (a Hillshade visualization might be easiest for most users). -For Output Detected Objects, specify a new Layer name, and this will be the layer on which the detections will be recorded and displayed. -For Model Definition, use Q2000.dlpk. -Unless you want to run "arguments", you can leave the Arguments boxes as their Default. -I suggest checking the box (On) for Non Max suppression because it will reduce the amount of overlapping detections if target objects are located very close to each other, and I suggest a Non-Maximum Suppression (NMS) ratio of 0.5. This will tend to suppress detections that overlap by more than 50 percent. -I suggest you use Pixel Space unchecked (Off), since it is for an unrelated sort of object detection. -Before you click run, open the "Environments" tab (at the top of the window, next to the "Parameters" tab). Leave all the settings at their defaults there - except scroll down to the bottom of Parameters tab to the section called "Processor Type", pull down the Processor type pull-down, and choose GPU (it is set to CPU by default). -Then click Run and it will generate a new layer showing its detections as bounding boxes around target objects. You can see details for each detection by opening the Attribute Table on the new layer. You can see a screen capture of such a configuration in the image called Q200DemoScreenCap.jpg, included in this site's dataset, showing a detection on G-LiHT transect Yucatan_South_GLAS_395 near Pixoyal, with a detection of a Maya staircase highlighted on the display, and its corresponding information highlighted in the Attribute Table for it.
- Creator/Author:
- Britton, Benjamin
- Submitter:
- Benjamin Britton
- Date Uploaded:
- 08/23/2025
- Date Modified:
- 11/13/2025
- Date Created:
- March 15, 2025
- License:
- Public Domain Mark 1.0
- Type:
- Collection
- Description/Abstract:
- This is a digitized collection of slides that were presented by Walter E. Langsam on the Cincinnati architecture firm Desjardins and Hayward
- Creator/Author:
- Meyer, Elizabeth A. and Langsam, Walter E.
- Submitter:
- Elizabeth A. Meyer
- License:
- All rights reserved
- Type:
- Image
- Description/Abstract:
- test
- Creator/Author:
- Scherz, Thomas
- Submitter:
- Thomas Scherz
- Date Uploaded:
- 09/29/2025
- Date Modified:
- 09/29/2025
- License:
- Attribution 4.0 International
20. How can a mobile living wall improve well-being and emotional health in palliative care settings?
- Type:
- Document
- Description/Abstract:
- This capstone explores how a calming mobile living wall can improve the well-being and emotional health of individuals with ALS in palliative care, as well as those who care for them. This project examines a mobile green wall as an adaptable solution that introduces the benefits of green design into various spaces within a care facility. The goals of this project are (1) to better understand how mobile green walls enhance users’ senses, thus reducing anxiety and influencing mood and stress and (2) to gain insight into a mobile green wall’s overall impact in palliative care environments.
- Creator/Author:
- Appel, Katie
- Submitter:
- Katie Appel
- Date Uploaded:
- 04/09/2025
- Date Modified:
- 09/23/2025
- Date Created:
- 2025-04-09
- License:
- Attribution 4.0 International